Detail publikace

SoluProt: Prediction of Protein Solubility

HON, J. MARUŠIAK, M. MARTÍNEK, T. ZENDULKA, J. BEDNÁŘ, D. DAMBORSKÝ, J.

Originální název

SoluProt: Prediction of Protein Solubility

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

Protein solubility poses a major bottleneck in production of many therapeutic and industrially attractive proteins. Experimental solubilization attempts are plagued by relatively low success rates and often lead to the loss of biological activity. Therefore, any advance in computational prediction of protein solubility may reduce the cost of experimental studies significantly. Here, we propose a novel software tool SoluProt for prediction of solubility from protein sequence based on machine learning and TargetTrack database. SoluProt achieved the best accuracy 58.2% and AUC 0.61 of all available tools at an independent balanced test set derived from NESG database. While the absolute prediction performance is rather low, SoluProt can still help to reduce costs of experimental studies significantly by efficient prioritization of protein sequences. The main SoluProt contribution lies in improved preprocessing of noisy training data and sensible selection of sequence features included in the prediction model.

Klíčová slova

protein, solubility, prediction, machine-learning

Autoři

HON, J.; MARUŠIAK, M.; MARTÍNEK, T.; ZENDULKA, J.; BEDNÁŘ, D.; DAMBORSKÝ, J.

Vydáno

17. 8. 2018

Nakladatel

Brno University of Technology

Místo

Brno

ISBN

978-80-214-5679-2

Kniha

DAZ & WIKT 2018 Proceedings

Strany od

261

Strany do

265

Strany počet

5

URL

BibTex

@inproceedings{BUT155085,
  author="Jiří {Hon} and Martin {Marušiak} and Tomáš {Martínek} and Jaroslav {Zendulka} and David {Bednář} and Jiří {Damborský}",
  title="SoluProt: Prediction of Protein Solubility",
  booktitle="DAZ & WIKT 2018 Proceedings",
  year="2018",
  pages="261--265",
  publisher="Brno University of Technology",
  address="Brno",
  isbn="978-80-214-5679-2",
  url="https://www.fit.vut.cz/research/publication/11808/"
}